Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations13104
Missing cells24
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory112.3 B

Variable types

Numeric8

Alerts

06299_MI1302.PV is highly overall correlated with 06299_MI1402.PV and 1 other fieldsHigh correlation
06299_MI1402.PV is highly overall correlated with 06299_MI1302.PV and 2 other fieldsHigh correlation
07633_HI0101.PV is highly overall correlated with 06299_MI1302.PV and 2 other fieldsHigh correlation
07781_MI1501.PV is highly overall correlated with 06299_MI1402.PV and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-09-29 18:21:57.008995
Analysis finished2024-09-29 18:22:01.118652
Duration4.11 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

06299_TI1302.PV
Real number (ℝ)

Distinct6619
Distinct (%)50.5%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean19.945667
Minimum11.207923
Maximum39.573353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.8 KiB
2024-09-29T20:22:01.164900image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum11.207923
5-th percentile12.465285
Q118.338572
median19.634083
Q321.669557
95-th percentile24.714569
Maximum39.573353
Range28.36543
Interquartile range (IQR)3.3309851

Descriptive statistics

Standard deviation4.2080687
Coefficient of variation (CV)0.21097658
Kurtosis4.9853672
Mean19.945667
Median Absolute Deviation (MAD)1.5884876
Skewness1.3401201
Sum261308.19
Variance17.707842
MonotonicityNot monotonic
2024-09-29T20:22:01.239018image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.82839394 69
 
0.5%
19.97589111 60
 
0.5%
20.02471924 59
 
0.5%
18.7795639 53
 
0.4%
20.00030518 52
 
0.4%
20.03692818 52
 
0.4%
18.14325333 51
 
0.4%
19.12137222 50
 
0.4%
18.889431 49
 
0.4%
18.10663223 49
 
0.4%
Other values (6609) 12557
95.8%
ValueCountFrequency (%)
11.20792294 1
 
< 0.1%
11.24454498 1
 
< 0.1%
11.25675201 1
 
< 0.1%
11.32999706 2
 
< 0.1%
11.36661911 1
 
< 0.1%
11.37882614 2
 
< 0.1%
11.39103413 5
< 0.1%
11.42765617 1
 
< 0.1%
11.4398632 5
< 0.1%
11.45207024 4
< 0.1%
ValueCountFrequency (%)
39.57335281 2
< 0.1%
39.43907166 3
< 0.1%
39.31699753 3
< 0.1%
39.23154449 1
 
< 0.1%
39.20713043 1
 
< 0.1%
39.1949234 2
< 0.1%
39.09726334 1
 
< 0.1%
39.0850563 1
 
< 0.1%
39.07284927 3
< 0.1%
39.0118103 1
 
< 0.1%

06299_MI1302.PV
Real number (ℝ)

HIGH CORRELATION 

Distinct10749
Distinct (%)82.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean59.758269
Minimum17.899105
Maximum93.908478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.8 KiB
2024-09-29T20:22:01.313542image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum17.899105
5-th percentile34.754897
Q146.750788
median61.750085
Q371.920933
95-th percentile83.494193
Maximum93.908478
Range76.009373
Interquartile range (IQR)25.170145

Descriptive statistics

Standard deviation15.538909
Coefficient of variation (CV)0.26002943
Kurtosis-0.9043572
Mean59.758269
Median Absolute Deviation (MAD)11.777012
Skewness-0.14107179
Sum782833.33
Variance241.45769
MonotonicityNot monotonic
2024-09-29T20:22:01.391534image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.89346313 14
 
0.1%
72.47840881 13
 
0.1%
72.28308868 13
 
0.1%
73.13761139 11
 
0.1%
73.30850983 11
 
0.1%
72.77138519 11
 
0.1%
71.55064392 11
 
0.1%
71.96569824 10
 
0.1%
73.67473602 10
 
0.1%
72.64931488 10
 
0.1%
Other values (10739) 12986
99.1%
ValueCountFrequency (%)
17.89910507 1
< 0.1%
18.68037987 1
< 0.1%
19.07101631 1
< 0.1%
19.601798 1
< 0.1%
19.94994926 1
< 0.1%
20.30066389 1
< 0.1%
20.45119954 1
< 0.1%
20.60577625 1
< 0.1%
20.74790183 1
< 0.1%
21.61015701 1
< 0.1%
ValueCountFrequency (%)
93.90847834 1
< 0.1%
93.82305145 1
< 0.1%
93.66567732 1
< 0.1%
93.50294156 1
< 0.1%
93.31034088 1
< 0.1%
93.15881296 1
< 0.1%
93.04177856 1
< 0.1%
92.876893 1
< 0.1%
92.7724615 1
< 0.1%
92.72133739 1
< 0.1%

06299_TI1402.PV
Real number (ℝ)

Distinct7062
Distinct (%)53.9%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21.225364
Minimum14.017152
Maximum38.340405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.8 KiB
2024-09-29T20:22:01.471432image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum14.017152
5-th percentile18.192083
Q119.103062
median20.537432
Q322.182369
95-th percentile29.303873
Maximum38.340405
Range24.323253
Interquartile range (IQR)3.0793074

Descriptive statistics

Standard deviation3.3489466
Coefficient of variation (CV)0.15778041
Kurtosis6.7804075
Mean21.225364
Median Absolute Deviation (MAD)1.5211966
Skewness2.3633703
Sum278073.5
Variance11.215444
MonotonicityNot monotonic
2024-09-29T20:22:01.543059image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.6559639 59
 
0.5%
18.70479393 51
 
0.4%
18.50947571 49
 
0.4%
20.52522278 45
 
0.3%
18.75514984 44
 
0.3%
18.74141693 43
 
0.3%
18.6925869 43
 
0.3%
18.53389168 43
 
0.3%
18.71700096 42
 
0.3%
18.64375687 41
 
0.3%
Other values (7052) 12641
96.5%
ValueCountFrequency (%)
14.01715183 1
< 0.1%
14.62752151 1
< 0.1%
14.96932888 1
< 0.1%
15.17685509 1
< 0.1%
15.29892921 1
< 0.1%
15.48203945 1
< 0.1%
16.1168251 1
< 0.1%
16.23889923 1
< 0.1%
16.27552032 1
< 0.1%
16.31214333 1
< 0.1%
ValueCountFrequency (%)
38.34040451 1
 
< 0.1%
38.23053741 1
 
< 0.1%
38.15729141 2
< 0.1%
38.12067032 1
 
< 0.1%
38.09625626 1
 
< 0.1%
38.04742432 2
< 0.1%
38.03521729 2
< 0.1%
37.97418213 1
 
< 0.1%
37.82769394 1
 
< 0.1%
37.80327606 3
< 0.1%

06299_MI1402.PV
Real number (ℝ)

HIGH CORRELATION 

Distinct11185
Distinct (%)85.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean52.883192
Minimum16.216573
Maximum86.951979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.8 KiB
2024-09-29T20:22:01.612025image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum16.216573
5-th percentile29.585494
Q140.836264
median53.287868
Q366.390607
95-th percentile71.146362
Maximum86.951979
Range70.735406
Interquartile range (IQR)25.554342

Descriptive statistics

Standard deviation14.166843
Coefficient of variation (CV)0.26788933
Kurtosis-1.2015206
Mean52.883192
Median Absolute Deviation (MAD)12.892157
Skewness-0.24230749
Sum692822.7
Variance200.69944
MonotonicityNot monotonic
2024-09-29T20:22:01.685479image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.227211 19
 
0.1%
65.44694519 18
 
0.1%
65.52018738 16
 
0.1%
65.49577332 16
 
0.1%
65.15396881 15
 
0.1%
65.83757782 15
 
0.1%
65.34928131 14
 
0.1%
65.73992157 14
 
0.1%
65.00747681 13
 
0.1%
65.64226532 13
 
0.1%
Other values (11175) 12948
98.8%
ValueCountFrequency (%)
16.21657311 1
< 0.1%
17.02229716 1
< 0.1%
17.41081047 1
< 0.1%
17.47689982 1
< 0.1%
17.51054024 1
< 0.1%
17.79036917 1
< 0.1%
17.89910507 1
< 0.1%
17.94822103 1
< 0.1%
18.37615232 1
< 0.1%
18.39557258 1
< 0.1%
ValueCountFrequency (%)
86.95197932 1
< 0.1%
85.82075852 1
< 0.1%
85.62056204 1
< 0.1%
85.61448514 1
< 0.1%
85.32232053 1
< 0.1%
83.17838947 1
< 0.1%
82.65174885 1
< 0.1%
82.33099952 1
< 0.1%
82.07647705 1
< 0.1%
82.02844395 1
< 0.1%

07633_TI0601.PV
Real number (ℝ)

Distinct3644
Distinct (%)27.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean21.153857
Minimum17.462379
Maximum26.645691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.8 KiB
2024-09-29T20:22:01.759029image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum17.462379
5-th percentile19.184029
Q120.225693
median21.137154
Q322.037758
95-th percentile23.245804
Maximum26.645691
Range9.1833115
Interquartile range (IQR)1.8120651

Descriptive statistics

Standard deviation1.252494
Coefficient of variation (CV)0.05920878
Kurtosis-0.13463855
Mean21.153857
Median Absolute Deviation (MAD)0.91145325
Skewness0.229271
Sum277178.98
Variance1.5687413
MonotonicityNot monotonic
2024-09-29T20:22:01.828678image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.75376129 114
 
0.9%
21.00694275 88
 
0.7%
20.61270142 79
 
0.6%
22.05584717 78
 
0.6%
21.40480042 77
 
0.6%
20.48249054 76
 
0.6%
21.80265808 75
 
0.6%
21.14800262 75
 
0.6%
21.14438629 75
 
0.6%
20.88034821 70
 
0.5%
Other values (3634) 12296
93.8%
ValueCountFrequency (%)
17.46237946 1
 
< 0.1%
17.59259033 1
 
< 0.1%
17.71918488 2
< 0.1%
17.73365021 1
 
< 0.1%
17.73726654 1
 
< 0.1%
17.84577179 1
 
< 0.1%
17.85662842 3
< 0.1%
17.86024475 1
 
< 0.1%
17.96151733 1
 
< 0.1%
17.979599 1
 
< 0.1%
ValueCountFrequency (%)
26.64569092 1
< 0.1%
26.54635981 1
< 0.1%
26.39611816 1
< 0.1%
26.3563385 1
< 0.1%
26.25144196 1
< 0.1%
26.13932037 1
< 0.1%
25.99464417 1
< 0.1%
25.94400787 1
< 0.1%
25.88274799 1
< 0.1%
25.73834419 1
< 0.1%

07633_HI0101.PV
Real number (ℝ)

HIGH CORRELATION 

Distinct6242
Distinct (%)47.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean44.195312
Minimum20.525896
Maximum60.445585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.8 KiB
2024-09-29T20:22:01.896976image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum20.525896
5-th percentile32.622275
Q140.960716
median44.865162
Q348.216869
95-th percentile53.037155
Maximum60.445585
Range39.919688
Interquartile range (IQR)7.2561533

Descriptive statistics

Standard deviation6.0707025
Coefficient of variation (CV)0.13736078
Kurtosis0.63843714
Mean44.195312
Median Absolute Deviation (MAD)3.6157417
Skewness-0.66258929
Sum579091.18
Variance36.853428
MonotonicityNot monotonic
2024-09-29T20:22:01.974043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.18894577 28
 
0.2%
45.51258469 26
 
0.2%
45.7910881 24
 
0.2%
45.24855423 24
 
0.2%
46.32638931 24
 
0.2%
45.37514496 23
 
0.2%
46.04788589 22
 
0.2%
46.05512238 22
 
0.2%
45.10749435 22
 
0.2%
44.70963669 21
 
0.2%
Other values (6232) 12867
98.2%
ValueCountFrequency (%)
20.52589607 1
< 0.1%
21.05396461 1
< 0.1%
21.32523155 1
< 0.1%
21.37602078 1
< 0.1%
21.50969315 1
< 0.1%
21.53139496 1
< 0.1%
21.73032379 1
< 0.1%
21.75925827 2
< 0.1%
21.90798347 1
< 0.1%
22.05096301 1
< 0.1%
ValueCountFrequency (%)
60.44558457 1
 
< 0.1%
60.14515995 1
 
< 0.1%
59.47265625 1
 
< 0.1%
59.34244537 1
 
< 0.1%
59.21947098 1
 
< 0.1%
59.20500565 1
 
< 0.1%
59.07479477 3
< 0.1%
59.06032944 1
 
< 0.1%
59.05243494 1
 
< 0.1%
58.95182037 1
 
< 0.1%

07781_TI1501.PV
Real number (ℝ)

Distinct1949
Distinct (%)14.9%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20.309929
Minimum19.399305
Maximum26.199074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.8 KiB
2024-09-29T20:22:02.048483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum19.399305
5-th percentile19.630606
Q120.083971
median20.288324
Q320.426504
95-th percentile21.241377
Maximum26.199074
Range6.7997684
Interquartile range (IQR)0.34253311

Descriptive statistics

Standard deviation0.48786895
Coefficient of variation (CV)0.024021204
Kurtosis9.230496
Mean20.309929
Median Absolute Deviation (MAD)0.19819069
Skewness2.237798
Sum266039.76
Variance0.23801612
MonotonicityNot monotonic
2024-09-29T20:22:02.120823image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.42650414 712
 
5.4%
20.22395706 527
 
4.0%
20.3614006 517
 
3.9%
20.08651543 381
 
2.9%
20.15885353 368
 
2.8%
20.2890625 348
 
2.7%
20.49884224 257
 
2.0%
20.49160767 225
 
1.7%
19.60908508 225
 
1.7%
20.0214119 209
 
1.6%
Other values (1939) 9330
71.2%
ValueCountFrequency (%)
19.39930534 1
 
< 0.1%
19.46440887 7
 
0.1%
19.47164345 35
0.3%
19.47345161 2
 
< 0.1%
19.47730134 1
 
< 0.1%
19.48249435 1
 
< 0.1%
19.48791885 1
 
< 0.1%
19.49153519 3
 
< 0.1%
19.49515343 1
 
< 0.1%
19.49696159 1
 
< 0.1%
ValueCountFrequency (%)
26.19907379 1
 
< 0.1%
24.16275978 1
 
< 0.1%
24 1
 
< 0.1%
23.54353333 1
 
< 0.1%
23.47662163 2
< 0.1%
23.41151619 1
 
< 0.1%
23.38258171 1
 
< 0.1%
23.27407455 3
< 0.1%
23.25418091 1
 
< 0.1%
23.20897102 1
 
< 0.1%

07781_MI1501.PV
Real number (ℝ)

HIGH CORRELATION 

Distinct5004
Distinct (%)38.2%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean47.745456
Minimum25.625723
Maximum59.357064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.8 KiB
2024-09-29T20:22:02.201591image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum25.625723
5-th percentile37.909483
Q145.38235
median49.348072
Q351.388938
95-th percentile52.955783
Maximum59.357064
Range33.731341
Interquartile range (IQR)6.0065879

Descriptive statistics

Standard deviation4.9302737
Coefficient of variation (CV)0.10326163
Kurtosis1.3318729
Mean47.745456
Median Absolute Deviation (MAD)2.5232316
Skewness-1.2547219
Sum625465.47
Variance24.307599
MonotonicityNot monotonic
2024-09-29T20:22:02.276006image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.19907379 107
 
0.8%
51.79397964 98
 
0.7%
51.92419052 85
 
0.6%
52.60416412 62
 
0.5%
51.38888931 60
 
0.5%
52.32928085 57
 
0.4%
50.99826431 57
 
0.4%
50.35902786 56
 
0.4%
51.93865585 56
 
0.4%
52.06886673 54
 
0.4%
Other values (4994) 12408
94.7%
ValueCountFrequency (%)
25.62572289 1
< 0.1%
25.91745399 1
< 0.1%
26.00911331 1
< 0.1%
26.08923146 1
< 0.1%
26.15161265 1
< 0.1%
26.4537951 1
< 0.1%
26.54803276 1
< 0.1%
26.59842179 1
< 0.1%
26.67462349 1
< 0.1%
26.67823982 1
< 0.1%
ValueCountFrequency (%)
59.35706422 1
< 0.1%
59.06394577 1
< 0.1%
58.94097137 1
< 0.1%
58.14987043 1
< 0.1%
57.89588936 1
< 0.1%
57.38200734 1
< 0.1%
57.33602142 1
< 0.1%
57.10848196 1
< 0.1%
57.09007263 1
< 0.1%
56.95167542 1
< 0.1%

Interactions

2024-09-29T20:22:00.408018image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.186704image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.670055image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.129357image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.562531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.019987image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.462576image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.944435image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.467031image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.265251image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.727201image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.182730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.620223image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.074593image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.521085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.001597image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.526645image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.325287image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.785321image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.239506image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.677346image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.132293image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.581518image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.062006image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.581228image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.384621image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.839128image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.288723image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.731784image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.184324image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.634530image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.116136image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.638868image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.442799image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.897739image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.345920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.789388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.240508image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.696715image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.175504image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.693498image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.495879image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.950468image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.395697image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.843518image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.289620image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.757306image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.228169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.754502image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.556464image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.010901image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.452691image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.903661image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.349329image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.819994image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.290181image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.816996image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:57.612446image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.069082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.507285image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:58.962798image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.403601image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:21:59.883276image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:22:00.345526image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-09-29T20:22:02.327836image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
06299_MI1302.PV06299_MI1402.PV06299_TI1302.PV06299_TI1402.PV07633_HI0101.PV07633_TI0601.PV07781_MI1501.PV07781_TI1501.PV
06299_MI1302.PV1.0000.746-0.467-0.1880.703-0.0490.4660.201
06299_MI1402.PV0.7461.000-0.075-0.4490.747-0.1000.5720.273
06299_TI1302.PV-0.467-0.0751.0000.200-0.031-0.0110.1160.091
06299_TI1402.PV-0.188-0.4490.2001.000-0.0450.0650.0110.023
07633_HI0101.PV0.7030.747-0.031-0.0451.000-0.2050.5830.268
07633_TI0601.PV-0.049-0.100-0.0110.065-0.2051.000-0.1330.055
07781_MI1501.PV0.4660.5720.1160.0110.583-0.1331.0000.097
07781_TI1501.PV0.2010.2730.0910.0230.2680.0550.0971.000

Missing values

2024-09-29T20:22:00.889817image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-29T20:22:00.973145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-29T20:22:01.060007image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

06299_TI1302.PV06299_MI1302.PV06299_TI1402.PV06299_MI1402.PV07633_TI0601.PV07633_HI0101.PV07781_TI1501.PV07781_MI1501.PV
DateTime
2023-03-15 00:00:00.00023.93158534.80143522.85416935.22342218.90190935.29007320.24739548.842590
2023-03-15 01:00:00.00023.74797834.40431521.56285336.61900118.78617135.45645120.24739548.712383
2023-03-15 02:00:00.00023.61573433.78935622.58097233.75999918.45383735.04050820.13346348.571323
2023-03-15 03:00:00.00019.90657038.74927519.63179738.06140218.42214033.69863920.13346348.296440
2023-03-15 04:00:00.00019.03777039.89581719.55182037.56048718.72691132.62803619.99782948.166233
2023-03-15 05:00:00.00018.82220640.20947019.90075936.46541618.82899231.95891219.86400448.036022
2023-03-15 06:00:00.00019.19803639.23655620.59846934.88391120.78832231.08967519.72656247.902199
2023-03-15 07:00:00.00019.32053339.14396520.91981834.47437220.52544630.30066019.96889547.902199
2023-03-15 08:00:00.00019.24562939.16586420.54963935.49238120.09714330.21918320.20218544.788048
2023-03-15 09:00:00.00019.34560940.20906420.57405337.01329020.17505630.73278220.20218545.601852
06299_TI1302.PV06299_MI1302.PV06299_TI1402.PV06299_MI1402.PV07633_TI0601.PV07633_HI0101.PV07781_TI1501.PV07781_MI1501.PV
DateTime
2024-09-10 15:00:00.00018.35078069.10916118.95529269.50855521.15161945.37514520.90139045.888538
2024-09-10 16:00:00.00018.58272070.28107518.86501765.00747721.27821446.02256820.76394744.007751
2024-09-10 17:00:00.00018.64375770.59846518.63155065.70394121.28906246.44574720.90139044.409229
2024-09-10 18:00:00.00018.50947670.96469118.21649768.81115221.41927346.53276620.90139045.223030
2024-09-10 19:00:00.00018.39960971.47740218.52114866.37470220.94418946.69892920.76394744.817940
2024-09-10 20:00:00.00018.59492971.29375818.87722464.95864920.86588346.83998921.30648245.888538
2024-09-10 21:00:00.00018.39960972.16600218.86501765.33557320.48610746.72424721.16904143.729252
2024-09-10 22:00:00.00018.28974371.67272218.69258768.99929920.10580346.18532920.62650543.327778
2024-09-10 23:00:00.00018.39960971.72155018.82839473.98653619.99782646.06235520.35704644.282639
2024-09-11 00:00:00.00018.16766972.23426118.46064670.11016819.70485745.53067020.22141344.951763